The following article updates the diffusion index, recession slack index, aggregate recession model, and aggregate peak-trough model through December 2012.

If you are new to Trader Edge and would like some additional background on the development of the Trader Edge recession models, I encourage you to read the following recession model articles in chronological order before continuing with the most recent update below.

Diffusion Index

The Trader Edge diffusion index equals the percentage of independent variables indicating a recession. There are a total of 16 explanatory variables, each with a unique look-back period and recession threshold. The resulting diffusion index and changes in the diffusion index are used to estimate the probit, logit, and neural network forecasting models.

The graph of the diffusion index from 1/1/2003 to 1/5/2013 is presented in Figure 1 below (in red - left axis). If you would like to view a graph of the earlier historical data (going back to 1960), please revisit A New Recession Slack Indicator. The gray shaded regions in Figure 1 below represent U.S. recessions as defined (after the fact) by the National Bureau of Economic Research (NBER). The value of the S&P 500 index is also included (in blue - right axis).

The diffusion index declined from 18.8% to 12.5% during the month of December. In other words, the percentage of explanatory variables indicating a recession declined from 18.8% to 12.5%. The economic data began to rebound in December from the temporary effects of Hurricane Sandy, which reduced the recession risk.

Please note that past estimates and index values will change whenever the historical data is revised. All forecasts and index calculations are always based on the latest revised data.

Figure 1: Diffusion Index 1-5-2013

Recession Slack Index

The Trader Edge recession slack index equals the median standardized deviation of the current value of the explanatory variables from their respective recession thresholds. The resulting value signifies the amount of slack or cushion relative to the recession threshold, expressed in terms of the number of standard deviations.

The latest recession slack index value was 1.03 standard deviations above the recession threshold, up from 0.75 last month. This is consistent with the decline in the diffusion index and a lower level of recession risk.

The gray shaded regions in Figure 2 below again represent U.S. recessions as defined by the NBER. The median recession slack index is depicted in purple and is plotted against the right axis, which is expressed as the number of standard deviations above the recession threshold.

The dark-red, horizontal line at 0.50 standard deviations denotes a possible warning threshold for the recession slack index. Many of the past recessions began when the recession slack index crossed below 0.50. Similarly, many of the past recessions ended when the recession slack index crossed above 0.0.

Figure 2: Recession Slack Index 1-5-2013

While it is useful to track the actual recession slack index values directly, the values are also used to generate the more intuitive probit and logit probability forecasts.

Aggregate Recession Probability Estimate

The Trader Edge aggregate recession model is the average of four models: the probit and logit models based on the diffusion index and the probit and logit models based on the recession slack index. The aggregate recession model estimates from 1/1/2003 to 1/5/2013 are depicted in Figure 3 below (red line - left vertical axis). The gray shaded regions represent NBER recessions and the blue line reflects the value of the S&P 500 index (right vertical axis). I suggest using a warning threshold of between 30-40% for the aggregate recession model (green horizontal line).

The aggregate recession model probability estimate for 1/5/2013 was 0.3%, which was down from 1.7% at the end of November.

Figure 3: Aggregate Recession Model 1-5-2013

Aggregate Peak-Trough Probability Estimate

The peak-trough model forecasts are different from the recession model. The peak-trough models estimate the probability of the S&P 500 being between the peak and trough associated with an NBER recession. The S&P 500 typically peaks before recessions begin and bottoms out before recessions end. As a result, it is far more difficult for the peak-trough model to fit this data and the model forecasts have larger errors than the recession model.

The Trader Edge aggregate peak-trough model equals the weighted-average of nine different models: the probit and logit models based on the diffusion index, the probit and logit models based on the recession slack index, and five neural network models.

The aggregate peak-trough model estimates from 1/1/2003 to 1/5/2013 are depicted in Figure 4 below, which uses the same format as Figure 3, except that the shaded regions represent the periods between the peaks and troughs associated with NBER recessions. The aggregate peak-trough model probability estimate for 1/5/2013 was 7.1%, which was down significantly from 19.3% at the end of November.

Figure 4: Aggregate Peak-Trough Model 1-5-2013

Conclusion

U.S. recession risk decreased significantly in December. The recession slack index was still comfortably above its 0.50 warning threshold and the aggregate model forecasts and diffusion index values were still well below their 30%-40% warning levels. As mentioned previously, the November data was adversely affected by hurricane Sandy. As expected, the related slowdown in economic activity is proving to be temporary.

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Brian Johnson

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About Brian Johnson

I have been an investment professional for over 30 years. I worked as a fixed income portfolio manager, personally managing over $13 billion in assets for institutional clients. I was also the President of a financial consulting and software development firm, developing artificial intelligence based forecasting and risk management systems for institutional investment managers.
I am now a full-time proprietary trader in options, futures, stocks, and ETFs using both algorithmic and discretionary trading strategies.
In addition to my professional investment experience, I designed and taught courses in financial derivatives for both MBA and undergraduate business programs on a part-time basis for a number of years.

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